Self-Configuring Hybrid Evolutionary Algorithm for Fuzzy Imbalanced Classification with Adaptive Instance Selection

Тип публикации: статья из журнала

Год издания: 2016

Ключевые слова: Fuzzy classification, instance selection, genetic fuzzy system, self-configuration

Аннотация: A novel approach for instance selection in classification problems is presented. This adaptive instance selection is designed to simultaneously decrease the amount of computation resources required and increase the classification quality achieved. The approach generates new training samples during the evolutionary process and changes the training set for the algorithm. The instance selection is guided by means of changing probabilities, so that the algorithm concentrates on problematic examples which are difficult to classify. The hybrid fuzzy classification algorithm with a self-configuration procedure is used as a problem solver. The classification quality is tested upon 9 problem data sets from the KEEL repository. A special balancing strategy is used in the instance selection approach to improve the classification quality on imbalanced datasets. The results prove the usefulness of the proposed approach as compared with other classification methods.

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Издание

Журнал: Journal of Artificial Intelligence and Soft Computing Research

Выпуск журнала: Т. 6, 3

Номера страниц: 173-188

ISSN журнала: 20832567

Авторы

  • Stanovov V.V. (Siberian State Aerospace University)
  • Semenkin E.S. (Siberian State Aerospace University)
  • Semenkina O.E. (Siberian State Aerospace University)

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